Who Are You? A Statistical Approach to Measuring User Authenticity

TitleWho Are You? A Statistical Approach to Measuring User Authenticity
Publication TypeConference Paper
Year of Publication2016
AuthorsFreeman, DM, Jain, S, Duermuth, M, Biggio, B, Giacinto, G
Conference NameProc. 23rd Annual Network & Distributed System Security Symposium (NDSS)
PublisherThe Internet Society

Passwords are used for user authentication by almost every Internet service today, despite a number of well-known weaknesses. Numerous attempts to replace passwords have failed, in part because changing users' behavior has proven to be difficult. One approach to strengthening password-based authentication without changing user experience is to classify login attempts into normal and suspicious activity based on a number of parameters such as source IP, geo-location, browser configuration, and time of day. For the suspicious attempts, the service can then require additional verification, e.g., by an additional phone-based authentication step. Systems working along these principles have been deployed by a number of Internet services but have never been studied publicly. In this work, we perform the first public evaluation of a classification system for user authentication. In particular: (i) we develop a statistical framework for identifying suspicious login attempts; (ii) we develop a fully functional prototype implementation that can be evaluated efficiently on large datasets; (iii) we validate our system on a sample of real-life login data from LinkedIn as well as simulated attacks, and demonstrate that a majority of attacks can be prevented by imposing additional verification steps on only a small fraction of users; and (iv) we provide a systematic study of possible attackers against such a system, including attackers targeting the classifier itself.

Citation Keyfreeman16-ndss
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